Air fuel ratio detector corrector for combustion engines using adaptive neurofuzzy networks

• Main Authors: Nidhi Arora, Swati Mehta
• Format: Article
• Language: English
• Published: Balikesir University 2013-07-01
• Series: An International Journal of Optimization and Control: Theories & Applications
• Subjects: Air-fuel ratio; adaptive learning systems; combustion engines; neuro-fuzzy network; detector; corrector
• Online Access: http://ijocta.balikesir.edu.tr/index.php/files/article/view/152/67

A perfect mix of the air and fuel in internal combustion engines is desirable for proper combustion of fuel with air. The vehicles running on road emit harmful gases due to improper combustion. This problem is severe in heavy vehicles like locomotive engines. To overcome this problem, generally an operator opens or closes the valve of fuel injection pump of locomotive engines to control amount of air going inside the combustion chamber, which requires constant monitoring. A model is proposed in this paper to alleviate combustion process. The method involves recording the time-varying flow of fuel components in combustion chamber. A Fuzzy Neural Network is trained for around 40 fuels to ascertain the required amount of air to form a standard mix to produce non-harmful gases and about 12 fuels are used for testing the network’s performance. The network then adaptively determines the additional/subtractive amount of air required for proper combustion. Mean square error calculation ensures the effectiveness of the network’s performance.